دانلود مقاله افسردگی، ساختار مغز و وضعیت اجتماعی اقتصادی
چکیده
1. مقدمه
2. روش ها
3. نتایج
4. بحث
5. محدودیت ها
6. کار آینده
7. نتیجه گیری
نقش منبع تامین مالی
منابع مالی
بیانیه مشارکت نویسنده CRediT
اعلامیه منافع رقابتی
قدردانی
مراجع
Abstract
1. Introduction
2. Methods
3. Results
4. Discussion
5. Limitations
6. Future work
7. Conclusion
Role of the funding source
Funding sources
CRediT authorship contribution statement
Declaration of competing interest
Acknowledgements
References
چکیده
زمینه و هدف: افسردگی ناشی از تعامل بین عوامل بیولوژیکی، اجتماعی و روانی است. ادبیات نشان می دهد که افسردگی با ساختار مغز غیرطبیعی مرتبط است و وضعیت اجتماعی-اقتصادی (SES) با افسردگی و ساختار مغز مرتبط است. با این حال، تحقیقات محدود، تعامل بین هر یک از این عوامل را در نظر می گیرد
روش‌ها: تجزیه و تحلیل رگرسیون چند متغیره با استفاده از داده‌های Biobank انگلستان روی 39995 شرکت‌کننده برای بررسی رابطه بین افسردگی و حجم مغز در 23 ناحیه قشر مغز برای کل نمونه انجام شد و سپس بر اساس جنسیت از هم جدا شدند. سپس بررسی کرد که آیا SES بر این رابطه تأثیر می گذارد یا خیر.
یافته‌ها: هشت ناحیه از 23 ناحیه مغز با افسردگی در کل جمعیت ارتباط منفی معنی‌داری داشتند. با این حال، زمانی که SES در تجزیه و تحلیل گنجانده شد، این روابط در هفت حوزه لغو شد. برای زنان، سه منطقه ارتباط منفی قابل توجهی با افسردگی داشتند که SES گنجانده نشد، اما تنها یک منطقه زمانی که آن را شامل شد. برای مردان، حجم کمتر در شش منطقه به طور قابل‌توجهی با افسردگی بالاتر بدون SES مرتبط بود، اما این رابطه در چهار منطقه با گنجاندن SES لغو شد. شکنج پیش مرکزی به طور قوی در تمام تجزیه و تحلیل ها با افسردگی مرتبط بود.
محدودیت ها: شرکت کنندگان با شرایطی که می تواند بر مغز تأثیر بگذارد، مستثنی نشدند. UK Biobank نماینده جمعیت عمومی نیست که ممکن است تعمیم پذیری را محدود کند. SES از آموزش و درآمد تشکیل شده بود که به طور جداگانه در نظر گرفته نمی شدند.
نتیجه‌گیری: SES بر رابطه بین افسردگی و حجم مغز قشر مغز تأثیر می‌گذارد. پزشکان و محققان بهداشتی باید هنگام کار با داده های تصویربرداری در این جمعیت ها این را در نظر بگیرند.
Abstract
Background
Depression results from interactions between biological, social, and psychological factors. Literature shows that depression is associated with abnormal brain structure, and that socioeconomic status (SES) is associated with depression and brain structure. However, limited research considers the interaction between each of these factors.
Methods
Multivariate regression analysis was conducted using UK Biobank data on 39,995 participants to examine the relationship between depression and brain volume in 23 cortical regions for the whole sample and then separated by sex. It then examined whether SES affected this relationship.
Results
Eight out of 23 brain areas had significant negative associations with depression in the whole population. However, these relationships were abolished in seven areas when SES was included in the analysis. For females, three regions had significant negative associations with depression when SES was not included, but only one when it was. For males, lower volume in six regions was significantly associated with higher depression without SES, but this relationship was abolished in four regions when SES was included. The precentral gyrus was robustly associated with depression across all analyses.
Limitations
Participants with conditions that could affect the brain were not excluded. UK Biobank is not representative of the general population which may limit generalisability. SES was made up of education and income which were not considered separately.
Conclusions
SES affects the relationship between depression and cortical brain volume. Health practitioners and researchers should consider this when working with imaging data in these populations.
Introduction
Mental health problems are significant public health issues within the UK and worldwide, with depression being one of the most prevalent. Research shows that there is a relationship between depression and brain structure (Tang et al., 2007; Kronmüller et al., 2009; Bos et al., 2018; Maggioni et al., 2019), and that social factors, including socioeconomic status (SES), are associated with both depression (Delgadillo et al., 2016; Marmot, 2010; Freeman et al., 2016) and changes in brain structure (Kweon et al., 2022; Colich et al., 2020; Jednoróg et al., 2012; Noble et al., 2015). However, these three factors are rarely all considered together. Given the impact of depression on both the individual and society, as discussed below, understanding the mechanisms of this disorder, to improve prevention and treatment options, is of critical importance.
Around 1 in 6 adults in England met the criteria for a common mental health disorder in the week prior to being surveyed (McManus et al., 2016). Mental health problems are one of the leading contributors to overall worldwide disease burden and are reported to account for 16 % of Disability Adjusted Life Years (DALYs, Arias et al., 2022). They confer substantial societal and economic implications with an estimated annual cost in England of £77 billion (Kirkwood et al., 2010). Depression is one of the most prevalent mental health problems, affecting approximately 5 % of adults (World Health Organization, WHO, 2023) and 280 million people globally (Institute of Health Metrics and Evaluation, 2023). Depression can be long-lasting with a single chronic episode, or recurrent, with many episodes over a long time, and has a substantial impact on an individual's ability to function and cope with daily life. The risk of suicide is reported to be about 15 times higher in those with depression than the general population (Cipriani et al., 2005). This is however likely to be an underestimate as many who die by suicide have undiagnosed depressive symptoms.
Conclusion
This study is, to our knowledge, the first study to examine the relationship between depression, cortical volume, and SES in a middle-aged to older sample of this size. Given that SES appears to impact the relationship between depression and brain structure, we posit that SES should be a consideration for clinicians and scientists working with imaging data in these populations. Future research involving large-scale longitudinal data is needed to elucidate the causal relationships between the factors examined in this study.